A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation
Yield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images...
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2024-11-01
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| author | Bernardo Lanza Davide Botturi Alessandro Gnutti Matteo Lancini Cristina Nuzzi Simone Pasinetti |
| author_facet | Bernardo Lanza Davide Botturi Alessandro Gnutti Matteo Lancini Cristina Nuzzi Simone Pasinetti |
| author_sort | Bernardo Lanza |
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| description | Yield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. The proposed algorithm is validated by analyzing its performance on a custom dataset. The number of berries, their mean radius, and the grape cluster volume are converted to millimeters and compared to reference values obtained through manual measurements. The validation experiment also analyzes the uncertainties of the parameters. Results show that the algorithm can reliably estimate the number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mo>−</mo><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>6</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and the radius of the visible portion of the grape clusters (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mn>0.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>7</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Instead, the volume estimated in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>px</mi><mn>3</mn></msup></semantics></math></inline-formula> results in a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mn>0.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>21</mn><mo>%</mo></mrow></semantics></math></inline-formula>, thus the corresponding volume in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>mm</mi><mn>3</mn></msup></semantics></math></inline-formula> is affected by high uncertainty. This analysis highlighted that half of the total uncertainty on the volume is due to the camera–object distance <i>d</i> and parameter <i>R</i> used to take into account the proportion of visible grapes with respect to the total grapes in the grape cluster. This issue is mostly due to the absence of a reliable depth measure between the camera and the grapes, which could be overcome by using depth sensors in combination with color images. Despite being preliminary, the results prove that the model and the metrological analysis are a remarkable advancement toward a reliable approach for directly estimating yield from 2D pictures in the field. |
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| institution | OA Journals |
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| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-b9e9c1f6fc704bd58c5a128d4f1d3e452025-08-20T02:27:39ZengMDPI AGSensors1424-82202024-11-012422730510.3390/s24227305A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume EstimationBernardo Lanza0Davide Botturi1Alessandro Gnutti2Matteo Lancini3Cristina Nuzzi4Simone Pasinetti5Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Mechanical and Industrial Engineering (DIMI), University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Information Engineering (DII), University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Medical and Surgical Specialties, Radiological Sciences, and Public Health (DSMC), University of Brescia, Viale Europa 11, 25123 Brescia, ItalyDepartment of Mechanical and Industrial Engineering (DIMI), University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Mechanical and Industrial Engineering (DIMI), University of Brescia, Via Branze 38, 25123 Brescia, ItalyYield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. The proposed algorithm is validated by analyzing its performance on a custom dataset. The number of berries, their mean radius, and the grape cluster volume are converted to millimeters and compared to reference values obtained through manual measurements. The validation experiment also analyzes the uncertainties of the parameters. Results show that the algorithm can reliably estimate the number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mo>−</mo><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>6</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and the radius of the visible portion of the grape clusters (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mn>0.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>7</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Instead, the volume estimated in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>px</mi><mn>3</mn></msup></semantics></math></inline-formula> results in a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>MPE</mi><mo>=</mo><mn>0.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>=</mo><mn>21</mn><mo>%</mo></mrow></semantics></math></inline-formula>, thus the corresponding volume in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>mm</mi><mn>3</mn></msup></semantics></math></inline-formula> is affected by high uncertainty. This analysis highlighted that half of the total uncertainty on the volume is due to the camera–object distance <i>d</i> and parameter <i>R</i> used to take into account the proportion of visible grapes with respect to the total grapes in the grape cluster. This issue is mostly due to the absence of a reliable depth measure between the camera and the grapes, which could be overcome by using depth sensors in combination with color images. Despite being preliminary, the results prove that the model and the metrological analysis are a remarkable advancement toward a reliable approach for directly estimating yield from 2D pictures in the field.https://www.mdpi.com/1424-8220/24/22/7305measurement sciencemachine visionviticulturedeep learningfruit countingfruit size estimation |
| spellingShingle | Bernardo Lanza Davide Botturi Alessandro Gnutti Matteo Lancini Cristina Nuzzi Simone Pasinetti A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation Sensors measurement science machine vision viticulture deep learning fruit counting fruit size estimation |
| title | A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation |
| title_full | A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation |
| title_fullStr | A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation |
| title_full_unstemmed | A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation |
| title_short | A Stride Toward Wine Yield Estimation from Images: Metrological Validation of Grape Berry Number, Radius, and Volume Estimation |
| title_sort | stride toward wine yield estimation from images metrological validation of grape berry number radius and volume estimation |
| topic | measurement science machine vision viticulture deep learning fruit counting fruit size estimation |
| url | https://www.mdpi.com/1424-8220/24/22/7305 |
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